Exploratory spatial analysis of food insecurity and diabetes: an application of multiscale geographically weighted regression

Abstract

ABSTRACT Both food insecurity and diabetes are important public health concerns. For example, diabetes remained a leading cause of mortality both in the United States (U.S.) and globally during the past decade. Meanwhile, an estimated 11% of households endured food insecurity in the U.S. in 2021 with higher rates in the Southeast region. While significant advancements have occurred in better understanding food insecurity, its relationship with diabetes has yielded mixed results. Due to these inconsistent findings, a better understanding of how food insecurity may be associated with diabetes, particularly at the county-level, can improve population health and well-being. This study advanced such an area by undertaking a cross-sectional observational study for the Southeastern region of the U.S. (e.g. Alabama, Arkansas, Mississippi, and Tennessee with 319 counties as the unit of analysis), an area disproportionately impacted by both food insecurity and diabetes. The overall design applied the socio-ecological perspective within a multiscale geographically weighted regression framework or MGWR. MGWR is a recent development and a more advanced approach which allowed adjustments for geographic space and scale. Results showed food insecurity (estimate of 0.28) was positively associated with diabetes but the relation varied in magnitude and significance across space (range of −0.10 to 1.19). That is, food insecurity exhibited a strong, positive association for northwestern Arkansas, a mild association for central Mississippi and Tennessee, and a weaker association for southern Alabama and eastern Tennessee. Households without internet access also exhibited a positive association (estimate of 0.15), as did convenience stores per thousand (estimate of 0.12). These findings add value to our understanding of how geographic space and scale matter when examining health. Public health practitioners can recognize such variations when devising targeted interventions for food insecurity and diabetes care.